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<a href="#pub-methods">Public Member Functions</a> &#124;
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<p><code>#include &lt;<a class="el" href="bpdc_8h_source.html">bpdc.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public Member Functions</h2></td></tr>
<tr class="memitem:a78ad1e1f3d76177d9a9ce42ffbdc8e2f"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a78ad1e1f3d76177d9a9ce42ffbdc8e2f">BPDC</a> (int numSensors, int numMotors)</td></tr>
<tr class="separator:a78ad1e1f3d76177d9a9ce42ffbdc8e2f"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a5aa934f3701069f3bc7e8175d92ba5ca"><td class="memItemLeft" align="right" valign="top">virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a5aa934f3701069f3bc7e8175d92ba5ca">~BPDC</a> ()</td></tr>
<tr class="separator:a5aa934f3701069f3bc7e8175d92ba5ca"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a50a5071ea857865a86576e74c69aede1"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a50a5071ea857865a86576e74c69aede1">setState</a> (Matrix motors)</td></tr>
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<tr class="memitem:a9bfa9ff04dd5115ff7abb834b55b39ce"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a9bfa9ff04dd5115ff7abb834b55b39ce">setDesiredOut</a> (Matrix futureSensor)</td></tr>
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<tr class="memitem:a7a854dad569a415405390c8cc67b0125"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a7a854dad569a415405390c8cc67b0125">predict</a> ()</td></tr>
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<tr class="memitem:ac9cb56a32a01b9a301aa5129834a73c2"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#ac9cb56a32a01b9a301aa5129834a73c2">getModelMatrix</a> (Matrix input)</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-attribs"></a>
Public Attributes</h2></td></tr>
<tr class="memitem:acf2123ec37f902bcdacd766376f3661c"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#acf2123ec37f902bcdacd766376f3661c">dt</a></td></tr>
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<tr class="memitem:ae28e6df7dbe289649e35facad3bc8572"><td class="memItemLeft" align="right" valign="top">double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#ae28e6df7dbe289649e35facad3bc8572">heta</a></td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-methods"></a>
Private Member Functions</h2></td></tr>
<tr class="memitem:a018adc454b0562c24b370460957eae1a"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a018adc454b0562c24b370460957eae1a">simulateNet</a> ()</td></tr>
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<tr class="memitem:a042277927c2583bb7a0cc366951c163b"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a042277927c2583bb7a0cc366951c163b">updateWeights</a> ()</td></tr>
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<tr class="memitem:aa71e52e68d069ca789813dfe7ed0f5d5"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#aa71e52e68d069ca789813dfe7ed0f5d5">kronneckerDelta</a> (int i, int k)</td></tr>
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Static Private Member Functions</h2></td></tr>
<tr class="memitem:a86c5fa382c8e1dd4e8ed5ba56c0c5fce"><td class="memItemLeft" align="right" valign="top">static double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a86c5fa382c8e1dd4e8ed5ba56c0c5fce">squash</a> (double node)</td></tr>
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<tr class="memitem:a023fd1821b0533d4c6610924040ed501"><td class="memItemLeft" align="right" valign="top">static double&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a023fd1821b0533d4c6610924040ed501">squash_derivative</a> (double node)</td></tr>
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Private Attributes</h2></td></tr>
<tr class="memitem:a958a3bcb34bd68bb5e0ad1816da42e82"><td class="memItemLeft" align="right" valign="top">std::vector&lt; Matrix &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a958a3bcb34bd68bb5e0ad1816da42e82">desiredSens</a></td></tr>
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<tr class="memitem:aef569d9244f88e7721e9ec1d40a22ae5"><td class="memItemLeft" align="right" valign="top">std::vector&lt; Matrix &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#aef569d9244f88e7721e9ec1d40a22ae5">errorBuff</a></td></tr>
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<tr class="memitem:a302ff738b16cfe38490f05884bbf689e"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a302ff738b16cfe38490f05884bbf689e">weights</a></td></tr>
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<tr class="memitem:a7bef215916138923f6c5cdddfb610877"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a7bef215916138923f6c5cdddfb610877">nodes</a></td></tr>
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<tr class="memitem:a242b8d0ff10d219018adc7b5dface3f5"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a242b8d0ff10d219018adc7b5dface3f5">inputWeights</a></td></tr>
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<tr class="memitem:a05f1ea7cce927d3e4223d26fbd803dbe"><td class="memItemLeft" align="right" valign="top">Matrix&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a05f1ea7cce927d3e4223d26fbd803dbe">inputNodes</a></td></tr>
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<tr class="memitem:a3fff4d746981fe91a9bcd047f16404c0"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a3fff4d746981fe91a9bcd047f16404c0">inputNum</a></td></tr>
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<tr class="memitem:a1efa2e4e1ef509c8e6b068a264c70548"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a1efa2e4e1ef509c8e6b068a264c70548">outputNum</a></td></tr>
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<tr class="memitem:a895c192c71e4c91691c61cf2dcdd965e"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classBPDC.html#a895c192c71e4c91691c61cf2dcdd965e">reservoirNum</a></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>File: <a class="el" href="bpdc_8h.html">bpdc.h</a></p>
<p>Class that implements the Back-propagation Decorellation learning rule (<a class="el" href="classBPDC.html">BPDC</a>) in Recurrent Neural Networks as presented in section 3.1.2 of the thesis.</p>
<p><a class="el" href="classBPDC.html">BPDC</a> is based on the decorrelation of networks' nodes value and the backpropagation of the errors occured in the last two time steps</p>
<dl class="section author"><dt>Author</dt><dd>: Athanasios Polydoros </dd></dl>
<dl class="section version"><dt>Version</dt><dd>: 1.0 Created on 01 July 2013, 15:17 </dd></dl>
</div><h2 class="groupheader">Constructor &amp; Destructor Documentation</h2>
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          <td class="memname">BPDC::BPDC </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>numSensors</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>numMotors</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
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<p>Class constructor that initialise the members of class. Has to be called in the controller's init method.</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">num_Sensors</td><td>The number of robot sensors, used as network's outputs </td></tr>
    <tr><td class="paramname">num_Motors</td><td>The number of motors, used as inputs </td></tr>
  </table>
  </dd>
</dl>

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          <td class="memname">BPDC::~BPDC </td>
          <td>(</td>
          <td class="paramname">)</td><td></td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
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<p>Class destructor </p>

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<h2 class="groupheader">Member Function Documentation</h2>
<a class="anchor" id="ac9cb56a32a01b9a301aa5129834a73c2"></a>
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          <td class="memname">Matrix BPDC::getModelMatrix </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>input</em>)</td><td></td>
          <td></td>
        </tr>
      </table>
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<p>Calculate the jacobian matrix of the network. The derivative of outputs wrt inputs. Should be called after activation of network</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">the</td><td>input nodes values </td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>MxM Jacobian matrix </dd></dl>

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      <table class="memname">
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          <td class="memname">int BPDC::kronneckerDelta </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>i</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>k</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
  </td>
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<p>Method that implements the Kronnecker delta </p>
<dl class="section return"><dt>Returns</dt><dd>1 if i=0 , otherwise 0 </dd></dl>

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          <td class="memname">Matrix BPDC::predict </td>
          <td>(</td>
          <td class="paramname">)</td><td></td>
          <td></td>
        </tr>
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<p>Predicts the future sensor values based on the current motor commands (inputs).</p>
<dl class="section return"><dt>Returns</dt><dd>Matrix that contains the predicted sensory values. </dd></dl>

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          <td class="memname">void BPDC::setDesiredOut </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>futureSensor</em>)</td><td></td>
          <td></td>
        </tr>
      </table>
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<p>Sets the desired output of the network. This method is called in controler's method : learn</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">fut_sensor</td><td>the robot's sensory values </td></tr>
  </table>
  </dd>
</dl>

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          <td class="memname">void BPDC::setState </td>
          <td>(</td>
          <td class="paramtype">Matrix&#160;</td>
          <td class="paramname"><em>motors</em>)</td><td></td>
          <td></td>
        </tr>
      </table>
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<p>Set the current inputs of the network</p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramname">motors</td><td>The values of robot's motors </td></tr>
  </table>
  </dd>
</dl>

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          <td class="memname">Matrix BPDC::simulateNet </td>
          <td>(</td>
          <td class="paramname">)</td><td></td>
          <td></td>
        </tr>
      </table>
  </td>
  <td class="mlabels-right">
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<p>Calculate the network's activations</p>
<dl class="section return"><dt>Returns</dt><dd>Matrix with the values of each node activation </dd></dl>

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          <td class="memname">double BPDC::squash </td>
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<p>The squashing function of the network. the hyperbolic tangent used (tanh)</p>
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<p>The derivative of squashing function of the network</p>
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<p>Update weights based on <a class="el" href="classBPDC.html">BPDC</a> learning rule. </p>

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<h2 class="groupheader">Member Data Documentation</h2>
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<p>History of the last two desired outputs </p>

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<p>time frame, dt=1 for discrete time </p>

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<p>regularization term (0.001) </p>

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<p>History of the last two errors </p>

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<p>Learning rate </p>

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<p>The values of input nodes </p>

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<p>The number of input nodes </p>

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<p>Matrix with the values of weights that connect the input with the reservoir </p>

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<p>The values of nodes before activation of the network </p>

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<p>The number of output nodes </p>

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<p>The number of reservoir nodes </p>

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<p>Matrix containing the weights ofthe network </p>

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<hr/>The documentation for this class was generated from the following files:<ul>
<li><a class="el" href="bpdc_8h_source.html">bpdc.h</a></li>
<li><a class="el" href="bpdc_8cpp.html">bpdc.cpp</a></li>
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